Geospatial imaging system providing segmentation and classification features and related methods

ABSTRACT

A geospatial imaging system may include a geospatial data storage device configured to store a geospatial dataset including geospatial data points. A processor may cooperate with the geospatial data storage device to determine segments within the geospatial dataset, with each segment including neighboring geospatial data points within the geospatial dataset sharing a common geometric characteristic from among different geometric characteristics. The processor may further determine border geospatial data points of adjacent segments, compare the border geospatial data points of the adjacent segments to determine bare earth segments having respective heights below those of the border geospatial data points of adjacent segments, and classify geospatial data points within each bare earth segment as bare earth geospatial data points.

TECHNICAL FIELD

The present invention relates to the field of topography, and, moreparticularly, to a system and related methods for generatingtopographical models.

BACKGROUND

Topographical models of geographical areas may be used for manyapplications. For example, topographical models may be used in flightsimulators. Furthermore, topographical models of man-made structures(e.g., cities) may be extremely helpful in applications such as cellularantenna placement, urban planning, disaster preparedness and analysis,and mapping, for example.

Various types and methods for making topographical models are presentlybeing used. One common topographical model is the digital elevation map(DEM). A DEM is a sampled matrix representation of a geographical areawhich may be generated in an automated fashion by a computer. In a DEM,coordinate points are made to correspond with a height value. DEMs aretypically used for modeling terrain where the transitions betweendifferent elevations (e.g., valleys, mountains, etc.) are generallysmooth from one to a next. That is, DEMs typically model terrain as aplurality of curved surfaces and any discontinuities therebetween arethus “smoothed” over. Thus, in a typical DEM no distinct objects arepresent on the terrain.

One particularly advantageous 3D site modeling product is RealSite® fromthe present Assignee Harris Corp. RealSite® may be used to registeroverlapping images of a geographical area of interest, and extract highresolution DEMs using stereo and nadir view techniques. RealSite®provides a semi-automated process for making three-dimensional (3D)topographical models of geographical areas, including cities, that haveaccurate textures and structure boundaries. Moreover, RealSite® modelsare geospatially accurate. That is, the location of any given pointwithin the model corresponds to an actual location in the geographicalarea with very high accuracy. The data used to generate RealSite® modelsmay include aerial and satellite photography, electro-optical, infrared,and light detection and ranging (LIDAR).

Another similar system from Harris Corp. is LiteSite®. LiteSite® modelsprovide automatic extraction of ground, foliage, and urban digitalelevation models (DEMs) from LIDAR and IFSAR imagery. LiteSite® can beused to produce affordable, geospatially accurate, high-resolution 3-Dmodels of buildings and terrain.

Despite the advantages such approaches may provide in certainapplications, further advancements may be desirable for classifying anddisplaying different types of geospatial model data.

SUMMARY

A geospatial imaging system may include a geospatial data storage deviceconfigured to store a geospatial dataset including a plurality ofgeospatial data points. A processor may cooperate with the geospatialdata storage device to determine a plurality of segments within thegeospatial dataset, with each segment including a plurality ofneighboring geospatial data points within the geospatial dataset sharinga common geometric characteristic from among a plurality of differentgeometric characteristics. The processor may further determine bordergeospatial data points of adjacent segments, compare the bordergeospatial data points of the adjacent segments to determine bare earthsegments having respective heights below those of the border geospatialdata points of adjacent segments, and classify geospatial data pointswithin each bare earth segment as bare earth geospatial data points.

More particularly, the processor may be further configured to performsegment determination training based upon a set of reference segments.By way of example, the processor may include a support vector machine(SVM) configured to perform the segment determination training. Also byway of example, each reference segment may include at least fiftyneighboring geospatial data points.

The processor may be further configured to determine a confidence valueassociated with geospatial data points classified as bare earthgeospatial data points. Moreover, the geospatial imaging system mayfurther include a display, and the processor may be configured todisplay the geospatial data set on the display with the bare earthgeospatial data points having different colors, for example, indicatingrespective confidence values associated therewith. The processor mayalso be configured to display the geospatial data set on the displaywith the bare earth geospatial data points having different colors, forexample, than non-bare earth geospatial data points.

By way of example, the plurality of different geometric characteristicsmay include a constant elevation, a common rate of elevation change,etc. The geospatial data points may comprise LIDAR data points, forexample.

A related geospatial imaging method may include storing a geospatialdataset including a plurality of geospatial data points in a geospatialdata storage device. The method may further include using a processor todetermine a plurality of segments within the geospatial dataset, whereineach segment may include a plurality of neighboring geospatial datapoints within the geospatial dataset sharing a common geometriccharacteristic from among a plurality of different geometriccharacteristics. The processor may also be used to determine bordergeospatial data points of adjacent segments, compare the bordergeospatial data points of the adjacent segments to determine bare earthsegments having respective heights below those of the border geospatialdata points of adjacent segments, and classify geospatial data pointswithin each bare earth segment as bare earth geospatial data points.

A related non-transitory computer readable medium may havecomputer-executable instructions for causing a computer to perform stepsincluding storing a geospatial dataset including a plurality ofgeospatial data points in a geospatial data storage device, anddetermining a plurality of segments within the geospatial dataset, whereeach segment includes a plurality of neighboring geospatial data pointswithin the geospatial dataset sharing a common geometric characteristicfrom among a plurality of different geometric characteristics. The stepsmay further include determining border geospatial data points ofadjacent the segments, comparing the border geospatial data points ofthe adjacent segments to determine bare earth segments having respectiveheights below those of the border geospatial data points of adjacentsegments, and classifying geospatial data points within each bare earthsegment as bare earth geospatial data points.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a geospatial imaging system inaccordance with an example embodiment.

FIG. 2 is a flow diagram illustrating method aspects associated with thegeospatial imaging system of FIG. 1.

FIGS. 3-5 are a series of schematic geospatial data point cloud diagramsillustrating an example segment determination approach using the systemof FIG. 1.

FIG. 6 is a flow diagram illustrating method aspects associated with thesegment determination approach of FIGS. 3-5.

DETAILED DESCRIPTION

The present invention will now be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Likenumbers refer to like elements throughout.

Referring initially to FIGS. 1 and 2, a geospatial imaging system 30 andassociated method aspects are first descried. The system 30illustratively includes a geospatial data storage device 31 and aprocessor 32, such as a central processing unit (CPU) of a PC, Mac, orother computing workstation, for example. The geospatial data storagedevice 31 may be configured to store a geospatial dataset including aplurality of geospatial data points. Generally speaking, the processor32 may be implemented using appropriate hardware (e.g., amicroprocessor) and an associated non-transitory computer-readablemedium having computer-executable instructions for performing the notedoperations set forth herein. It should also be noted that the processingoperations described herein may also be distributed or shared betweenmultiple processing devices, and need not all be performed by a singledevice in all embodiments, as will be appreciated by those skilled inthe art.

The geospatial data points may be captured using a LIDAR device 33. Asseen in the example of FIG. 1, a LIDAR device 33 is carried by anairplane 34 and scans terrain with laser light 37 to determinegeo-referenced elevation point data from reflected laser light, as willbe appreciated by those skilled in the art. However, it should be notedthat geospatial data points may be captured by other approaches, such assynthetic aperture radar (SAR) or interferometric SAR (IFSAR), etc., andother airborne platforms (e.g., satellites, UAVs, helicopters, etc.) mayalso be used as well. As the terrain is scanned, geospatial data pointsare measured from the returned laser light for bare earth areas 35, aswell as non-bare earth areas, which may correspond to foliage, buildings36, or other man-made structures (e.g., towers, bridges etc.), forexample. The processor may generate a three-dimensional (3D) model ofthe geospatial dataset to be displayed on a display 38, for example, aswill be discussed further below.

Beginning at Block 41 of the flow diagram 40, the geospatial data pointsof the dataset collected by the LIDAR device 33 (or other collectiondevice(s)) may be stored in the geospatial data storage device 31 forprocessing, at Block 42. More particularly, the processor 32 maydetermine a plurality of segments within the geospatial dataset, atBlock 43. As will be discussed further below, a segment may include aplurality of neighboring geospatial data points within the geospatialdataset sharing a common geometric characteristic from among a pluralityof different geometric characteristics. The processor 32 may furtherdetermine border geospatial data points of adjacent segments, at Block44, and compare the border geospatial data points of the adjacentsegments to determine bare earth segments having respective heightsbelow those of the border geospatial data points of adjacent segments,at Block 45. The method may further illustratively include classifyinggeospatial data points within each bare earth segment as bare earthgeospatial data points, at Block 46.

The foregoing will be further understood with reference to an examplenow described with reference to FIGS. 3-6. In the flow diagram 60 (FIG.6), an input point cloud of geospatial data points may be collected orprovided (Block 61), such as from the LIDAR source 33 as describedabove, and stored in the geospatial data storage device 31 forprocessing. FIGS. 3-5 depict a hypothetical profile view of a pointcloud 50 including a gable roof house 51 with bare earth regions 52, 53on the left and right sides of the house, respectively.

In the present example, k nearest neighbor (kNN) graph formationprocessing is performed (Block 62) to construct a graph from the pointcloud 50, where each geospatial data point is a vertex in the graph andedges are formed by connecting a geospatial data point to each of its knearest neighbors. More particularly, P represents the set of all pointswithin the point cloud 50 as follows:

P={

x _(i) ,y _(i) ,z _(i)

|i∈[1,C]},

where <x_(i), y_(i), z_(i)> are the three-dimensional coordinates for apoint p_(i), and C is the total number of points in the point cloud. Aset of points N_(i) may include all points connected to point p_(i). ThekNN processing makes connections for all points within the point cloud50, and the points become vertices for a graph which may be weighted(Block 63) as follows:

ω_(i,j) =|z _(i) −z _(j)|,

where ω_(i,j) is the absolute difference in elevation between twogeospatial data points i and j connected by a given edge, and z_(i) andz_(j) are the elevations of geospatial data points i and j respectively.In the example shown in FIG. 3, the KNN processing looks for the nearesttwo neighbors to each point in each direction, and accordingly connectspoints p₁ with points p₂, p₃, p₄ and p₅ as the known nearest neighborsof p₁. In other words, the KNN set of points N₁ for the point p₁ is {p₂,p₃, p₄, p₅}, as indicated by the associated arrows. It should be notedthat in other embodiments different numbers of neighboring points may beincluded within the set N_(i) other than two.

After the nearest neighbor points and weights are determined, the pointswithin the point cloud 50 may be segmented using graph-basedsegmentation (Block 64). Generally speaking, a segment is a group ofpoints which share a common geometric characteristic, such as a constantelevation in the case of the bare earth regions 52, 53, and a commonrate of elevation change in the case of the gable roof house 51, as willbe appreciated by those skilled in the art. Other common geometriccharacteristics may also be used in different embodiments.

In the present example, S may represent the set of all segments asfollows:

s_(k)∈S where k=1, . . . , ∥S∥.

A term B(s_(k)) may represent the set of all border points of a givensegment. In the present example, all of the points in the house 51 aregrouped as a single segment with boundary points p₆ and p₈, although insome embodiments the house may be divided into separate segments.Features associated with the segments, such as an average relativeheight ARH, may then be determined (Block 65) for the given segment asfollows:

${{{ARH}\left( s_{k} \right)} = {\frac{1}{{B\left( s_{k} \right)}}{\sum\limits_{i \in {B{(s_{k})}}}\; {h\left( {i,s_{k}} \right)}}}},{where}$h(i, s_(k)) = z_(i) − z_(j|j ∈ N_(i)⋂j ∉ s_(k)).

In the example illustrated in FIG. 4, the difference in elevation ARHsfor the house segment and adjacent segments corresponding to the bareearth regions 52, 53 are indicated by the LARH arrows.

A percentage of points above neighbors for each segment, PRH_(high), maybe determined as follows:

${{{PRH}_{high}\left( s_{k} \right)} = {\frac{1}{{B\left( s_{k} \right)}}{\sum\limits_{i \in {B{(s_{k})}}}\; {\Phi \left( {i,s_{k}} \right)}}}},{where}$${\Phi \left( {i,s_{k}} \right)} = \left\{ \begin{matrix}\left. 1 \middle| {{h\left( {i,s_{k}} \right)} > 0} \right. \\\left. 0 \middle| {{otherwise}.} \right.\end{matrix} \right.$

In the example shown in FIG. 4, the segment corresponding to the house51 has 100% of its border points above neighboring segments for the bareearth regions 52, 53 with respect to elevation.

Another segment feature which may be determined is a segment border discdifference DD(s_(k)), as follows:

${{{DD}\left( s_{k} \right)} = {{\frac{1}{{B\left( s_{k} \right)}}{\sum\limits_{i \in {B{(s_{k})}}}\; z_{i}}} - {\min\limits_{z}\left( {M_{i}\left( s_{k} \right)} \right)}}},{where}$${M_{i}\left( s_{k} \right)} = \left\{ z_{j} \middle| \begin{matrix}{x_{j} \leq {x_{i} + {1\bigwedge}}} \\{x_{j} > {x_{i} - {1\bigwedge}}} \\{y_{j} \leq {y_{i} + {1\bigwedge}}} \\{y_{j} > {y_{i} - {1\bigwedge}}} \\{j \in {N_{i}\bigcap j} \notin {s_{k}.}}\end{matrix} \right.$

More particularly, for a given segment's border point i,

$\min\limits_{z}\left( {M_{i}\left( s_{k} \right)} \right)$

represents the minimum elevation of all points that do not belong to thegiven segment s_(k) and exist within a 2D (horizontal and vertical) onemeter radius of that given segment's border point. In the example shownin FIG. 5, point p₁₀ exists within a one meter radius from point p₆, andis the lowest point, with respect to its elevation, of all points thatfall within a one meter radius of point p₆. Similarly, point p₁₁ existswithin a one meter radius of point p₈, and has the lowest elevation ofall points falling within a one meter radius of point p₈. The discdifference for a given segment DD(s_(k)) is then the sum of thedifferences between the elevation of each border point and the

$\min\limits_{z}\left( {M_{i}\left( s_{k} \right)} \right)$

for those border points.

Using such features determined from a set of reference or trainingsegments, the processor 32 may optionally be trained through machinelearning techniques to provide enhanced segment recognition andaccuracy. By way of example, the processor 32 may include a supportvector machine (SVM) module which is trained based upon the providedtraining data (Block 67). To help ensure effective training, thetraining may be performed on features derived from segments which have apurity U(s_(k)) associated therewith of 90%, i.e., where 90% of thepoints in that segment have the same truth classification. While itshould be noted that other purity percentages may also be used indifferent embodiments, a relatively high threshold percentage may bedesirable in that segments may sometimes bleed over and include bothground and non-ground points, and higher thresholds may help avoidtraining on those undesired segments having bleed over.

Another feature of training on segmented data is the option to onlytrain on segments that have a minimum number of points therein. By wayof example, a minimum training segment point threshold may be at leastfifty points, although other threshold numbers of points may be used indifferent embodiments. Generally speaking, relatively small segments mayresult in misleading phenomenon, e.g., outliers.

In accordance with one example training configuration, for each segmentand the border points of nearby adjacent segments, and the lowest pointsin elevation within a 2D one-meter radius of each segment's borderpoints that do not belong to that segment, the following segment-basedfeatures may be calculated for each segment: (1) the segment's relativeheight—the average difference between the segment's border points'elevation and connected border points' elevation belonging to differentsegments; (2) the percentage of border points for a given segment thatexist above their neighboring border points belonging to other segments;and (3) the average height difference of the border points for a givensegment and the lowest elevation points belonging to another segment andexisting within a 2D one-meter radius of the given segment's borderpoints. These features may then be used to train the SVM module 66 toclassify geospatial data points within each segment as belonging toeither bare earth or non bare earth. It should be noted that less thanall three of the above-noted features may be used in some embodiments,and that other potential learning features may also be used in differentembodiments.

Using the training data and calculated segment features as input, theSVM module 66 may make classifications between segments, such as bareearth segments and non-bare earth segments (Block 68). This segmentationapproach helps preserves mapping to respective points. Moreparticularly, if a segment is classified as bare earth, all of thatsegment's points may be classified as bare earth. This mapping alsoenables a performance comparison to other point based classifiers, aswill be appreciated by those skilled in the art. By classifying entiresegments as having the same type of points (e.g., bare earth or non-bareearth), this may advantageously help analysts simplify the manual touchup process for a resulting terrain model, as will be appreciated bythose skilled in the art. As a result, less analyst time may be neededfor classifying point clouds, which results in reduced labor costs.

Another optional feature which may be particularly advantageous in someapplications is that the SVM module 66 may provide a confidence valueindicating a probability or margin of error with respect to whether apoint is bare earth or not, at Block 69. The confidence value may beembedded in a field of the model data. That is, the processed geospatialdata points may be used to generate a model of the terrain for viewingon the display 38, such as a digital elevation model (DEM) or digitalterrain model (DTM), for example, although other forms of models may beused in different embodiments. Generally speaking, the confidence valuemay represent how probable it is, based upon the classifier used, thetraining set of reference segments, and the features derived from thosesegments, that those geospatial data points are bare earth geospatialdata points, for example.

By way of example, one file format which may be used for the interchangeof 3D point cloud data is the LAS format (although other suitable dataformats may also be used). In the LAS format, information such as theabove-noted confidence values may be incorporated in a classificationfield which may be accessible by an analyst. Another option is that theconfidence data may be translated to intensity values for respectivepoints that are included in the file format, which would result in thepoints being displayed with different colors, brightness, etc., basedupon their respective confidence levels (see Block 48 of FIG. 2).

By way of example, a first color (e.g., blue) may indicate higherconfidence values on the one hand, and a second color (e.g., red) mayindicate lower confidence values on the other hand, where confidencevalues in between high and low probabilities would transition between adark blue and a dark red, for example (although other colorconfigurations may also be used in different embodiments). This may helpan analyst readily identify regions or areas where processing was moredifficult due to uneven terrain, etc., and thus what areas may requiremore manual revision by the analyst, and which areas require little orno attention by the analyst.

It should be noted that other display options for the data may also beprovided by the processor 32. For example, coloration or brightness,etc., of geospatial data points may be varied based upon theirrespective classification as bare earth or non-bare earth data points.That is, bare earth data points may be displayed using one color, andnon-bare earth data points may be displayed using another color, atBlock 48 of FIG. 2, which concludes the method illustrated therein(Block 49). In accordance with another example, data pointclassification and confidence values may both be indicated using datacoloration and brightness, such as by making bare earth points be of onecolor family (e.g., blue) and non bare earth data points be of anothercolor family (e.g., red), and the brightness of the points varied inaccordance with their respective confidence values.

In some embodiments, void filling may be performed on regions within thegeospatial dataset where missing or corrupted data points occur, atBlock 70. The associated model (e.g., DTM) with (or without) filledvoids may be generated for display, at Block 71, along with associatedabove-ground processing, at Block 72. Various approaches for generatingmodels and void filling, along with above ground calculation techniques,may be used, as will be appreciated by those skilled in the art. By wayof example, one such approach for inpainting data in geospatial modelsmay be found in U.S. Pat. No. 7,750,902 to Rahmes et al., which isassigned to the present Applicant and is hereby incorporated herein inits entirety by reference.

As a result of the above-described approach, the example configurationsadvantageously allow for geospatial data processing with little or nomanual input parameter tuning for different geographical regions. Thatis, most current approaches have one or more parameters that have to betuned for different sensors, different collection concept of operations(CONOPS), different terrain types (flat vs. hilly vs. mountainous) ordifferent building types. However, the above-described approach may bemore automated without the need for providing such input parameters fordifferent sensor types, etc. More particularly, the above-describedapproach has been found to be effective across multiple types of data,including both linear mode LIDAR and Geiger mode LIDAR data sets, forexample.

Similarly, the above-noted approach may also be effective for differentterrain scenarios, including relatively flat regions, mountainousscenes, barrio or dense urban scenes, and mountainous peaks, forexample. Similarly, this approach may also be effective for differentbuilding structures including barrios, commercial and residentialbuildings, urban buildings, etc.

Many modifications and other embodiments of the invention will come tothe mind of one skilled in the art having the benefit of the teachingspresented in the foregoing descriptions and the associated drawings.Therefore, it is understood that the invention is not to be limited tothe specific embodiments disclosed, and that modifications andembodiments are intended to be included within the scope of the appendedclaims.

That which is claimed is:
 1. A geospatial imaging system comprising: ageospatial data storage device configured to store a geospatial datasetcomprising a plurality of geospatial data points; and a processorcooperating with said geospatial data storage device to determine aplurality of segments within the geospatial dataset, each segmentcomprising a plurality of neighboring geospatial data points within thegeospatial dataset sharing a common geometric characteristic from amonga plurality of different geometric characteristics, determine bordergeospatial data points of adjacent segments, compare the bordergeospatial data points of the adjacent segments to determine bare earthsegments having respective heights below those of the border geospatialdata points of adjacent segments, and classify geospatial data pointswithin each bare earth segment as bare earth geospatial data points. 2.The geospatial imaging system of claim 1 wherein said processor isfurther configured to perform segment determination training based upona set of reference segments.
 3. The geospatial imaging system of claim 2wherein said processor comprises a support vector machine (SVM)configured to perform the segment determination training.
 4. Thegeospatial imaging system of claim 2 wherein each reference segmentcomprises at least fifty neighboring geospatial data points.
 5. Thegeospatial imaging system of claim 1 wherein said processor is furtherconfigured to determine a confidence value associated with geospatialdata points classified as bare earth geospatial data points.
 6. Thegeospatial imaging system of claim 4 further comprising a display; andwherein said processor is configured to display the geospatial data seton the display with the bare earth geospatial data points havingdifferent colors indicating respective confidence values associatedtherewith.
 7. The geospatial imaging system of claim 1 furthercomprising a display; and wherein said processor is configured todisplay the geospatial data set on the display along with the bare earthgeospatial data points having different colors than non-bare earthgeospatial data points.
 8. The geospatial imaging system of claim 1wherein one of the plurality of different geometric characteristicscomprises a constant elevation.
 9. The geospatial imaging system ofclaim 1 wherein one of the plurality of different geometriccharacteristics comprises a common rate of elevation change.
 10. Thegeospatial imaging system of claim 1 wherein the geospatial data pointscomprise LIDAR data points.
 11. A geospatial imaging method comprising:storing a geospatial dataset comprising a plurality of geospatial datapoints in a geospatial data storage device; and using a processor todetermine a plurality of segments within the geospatial dataset, eachsegment comprising a plurality of neighboring geospatial data pointswithin the geospatial dataset sharing a common geometric characteristicfrom among a plurality of different geometric characteristics, determineborder geospatial data points of adjacent segments, compare the bordergeospatial data points of the adjacent segments to determine bare earthsegments having respective heights below those of the border geospatialdata points of adjacent segments, and classify geospatial data pointswithin each bare earth segment as bare earth geospatial data points. 12.The method of claim 11 further comprising using the processor to performsegment determination training based upon a set of reference segments.13. The method of claim 11 further comprising using the processor todetermine a confidence value associated with geospatial data pointsclassified as bare earth geospatial data points.
 14. The method of claim13 further comprising using the processor to display the geospatial dataset on a display with the bare earth geospatial data points havingdifferent colors indicating respective confidence values associatedtherewith.
 15. The method of claim 11 further comprising using theprocessor to display the geospatial data set on a display with the bareearth geospatial data points having different colors than non-bare earthgeospatial data points.
 16. The method of claim 11 wherein one of theplurality of different geometric characteristics comprises a constantelevation.
 17. The method of claim 11 wherein one of the plurality ofdifferent geometric characteristics comprises a common rate of elevationchange.
 18. A non-transitory computer readable medium havingcomputer-executable instructions for causing a computer to perform stepscomprising: determining a plurality of segments within a geospatialdataset comprising a plurality of geospatial data points, each segmentcomprising a plurality of neighboring geospatial data points within thegeospatial dataset sharing a common geometric characteristic from amonga plurality of different geometric characteristics; determining bordergeospatial data points of adjacent the segments; comparing the bordergeospatial data points of the adjacent segments to determine bare earthsegments having respective heights below those of the border geospatialdata points of adjacent segments; and classifying geospatial data pointswithin each bare earth segment as bare earth geospatial data points. 19.The non-transitory computer readable medium of claim 18 further havingcomputer-executable instructions for causing the computer to performsegment determination training based upon a set of reference segments.20. The non-transitory computer readable medium of claim 18 furtherhaving computer-executable instructions for causing the computer todetermine a confidence value associated with geospatial data pointsclassified as bare earth geospatial data points.
 21. The non-transitorycomputer readable medium of claim 20 further having computer-executableinstructions for causing the computer to display the geospatial data seton a display with the bare earth geospatial data points having differentcolors indicating respective confidence values associated therewith.